Tag Archives: agile

With a full first draft of my book in the hands of the publishers, I’m hoping to get back to a more regular schedule of blogging. Frankly, I’m looking forward to it. It’s a lot less of a grind than the “everyday after work and all day on the weekends pace” that was needful for finishing “Measuring the Digital World”! I’ve also accumulated a fair number of ideas for things to talk about; some directly from the book and some from our ongoing practice.

The vast majority of “Measuring the Digital World” concerns topics I’ve blogged about many times: digital segmentation, functionalism, meta-data, voice-of-customer, and tracking user journeys. Essentially, the book proceeds by developing a framework for digital measurement that is independent of any particular tool, report or specific application. It’s an introduction not a bible, so it’s not like I covered tons of new ground. But, as will happen any time you try to voice what you know, some new understandings did emerge. I spent most of a chapter trying to articulate how the impact of self-selection and site structure can be handled analytically; this isn’t new exactly, but some of the concepts I ended up using were. Sections on rolling your own experiments with analytics not testing, and the idea of use-case demand elasticity and how to measure it, introduced concepts that crystallized for me only as I wrote them down. I’m looking forward to exploring those topics further.

At the same time, we’ve been making significant strides in our digital analytics practice that I’m eager to talk about. Writing a book on digital analytics has forced me to take stock not only of what I know, but also of where we are in our profession and industry. I really don’t know if “Measuring the Digital World” is any good or not (right now, at least, I am heartily sick of it), but I do know it’s ambitious. Its goal is nothing less than to establish a substantive methodology for digital analytics. That’s been needed for a long time. Far too often, analysts don’t understand how measurement in digital actually works and are oblivious to the very real methodological challenges it presents. Their ignorance results in a great deal of bad analysis; bad analysis that is either ignored or, worse, is used by the enterprise.

Even if we fixed all the bad analysis, however, the state of digital analytics in the enterprise would still be disappointing. Perhaps even worse, the state of digital in the enterprise is equally bad. And that’s really what matters. The vast majority of companies I observe, talk to, and work with, aren’t doing digital very well. Most of the digital experiences I study are poorly integrated with offline experiences, lack any useful personalization, have terribly inefficient marketing, are poorly optimized by channel and – if at all complex – harbor major usability flaws.

This isn’t because enterprises don’t invest in digital. They do. They spend on teams, tools and vendors for content development and deployment, for analytics, for testing, and for marketing. They spend millions and millions of dollars on all of these things. They just don’t do it very well.

Why is that?

Well, what happens is this:

Enterprises do analytics. They just don’t use analytics.

Enterprises have A/B testing tools and teams and they run lots of tests. They just don’t learn anything.

Enterprises talk about making data-driven decisions. They don’t really do it. And the people who do the most talking are the worst offenders.

Everyone has gone agile. But somehow nothing is.

Everyone says they are focused on the customer. Nobody really listens to them.

It isn’t about doing analytics or testing or voice of customer. It’s about finding ways to integrate them into the organization’s decision-making. In other words, to do digital well demands a fundamental transformation in the enterprise. It can’t be done on a business as usual basis. You can add an analytics team, build an A/B testing team, spend millions on attribution tools, Hadoop platforms, and every other fancy technology for content management and analytics out there. You can buy a great CMS with all the personalization capabilities you could ever demand. And almost nothing will change.

Analytics, testing, VoC, agile, customer-focus…these are the things you MUST do if you are going to do digital well. It isn’t that people don’t understand what’s necessary. Everyone knows what it takes. It’s that, by and large, these things aren’t being done in ways that drive actual change.

Having the right methodology for digital analytics is a (small) part of that. It’s a way to do digital analytics well. And digital analytics truly is essential to delivering great digital experiences. You can’t be great – or even pretty good – without it. But that’s clearly not enough. To do digital well requires a deeper transformation; it’s a transformation that forces the enterprise to blend analytics and testing into their DNA, and to use both at every level and around every decision in the digital channel.

That’s hard. But that’s what we’re focusing on this year. Not just on doing analytics, but on digital transformation. We’re figuring out how to use our team, our methods, and our processes to drive change at the most fundamental level in the enterprise – to do digital differently: to make decisions differently, to work differently, to deliver differently and, of course, to measure differently.

As we work through delivering on digital transformation, I plan to write about that journey as well: to describe the huge problems in the way most enterprises actually do digital, to describe how analytics and testing can be integrated deep into the organization, to show how measurement can be used to change the way organizations actually think about and understand their customers, and to show how method and process can be blended to create real change. We want to drive change in the digital experience and, equally, change in the controlling enterprise, for it is from the latter that the former must come if we are to deliver sustained success.

People have struggled with this (big) data provider model but Factual feels like it’s found a real (and valuable) niche. Would love to see more of this grow since external data is a huge miss in most big data systems.

Targeted VoC is a powerful (and totally neglected) tool for personalization. Facebook’s experience is entirely relevant to ANY content producer. I don’t know if I can take credit for this, but I suggested this to folks at Facebook a couple of years back!

An interesting discussion of the problems in identifying “likely” voters and the benefits of behavioral data integration. Food for thought in the enterprise world as well where the equivalent is often possible but rarely done.